Sigbjørn Løland Bore

• Bortoli, Marco; Bore, Sigbjørn Løland; Eisenstein, Odile & Cascella, Michele (2025). The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials. Journal of Catalysis. ISSN 0021-9517. 454. doi: 10.1016/j.jcat.2025.116619. Full text in Research Archive Show summary

  The Schlenk equilibrium is an essential characteristic of the Grignard reagents. However, its quantitative aspects remain poorly understood. In this work, we perform molecular dynamics simulations to characterise how the Schlenk reaction is affected by varying the alkyl R groups (Me, Et, i-Pr, t-Bu). For each R group, the reactivity profiles were obtained through enhanced sampling on fully solvated dinuclear species, leveraging a newly developed machine learning potential trained on ab initio data. While the topologies of the Helmholtz energy maps are qualitatively similar, the energy ranges vary significantly with the R groups. Bulkier R groups disfavour higher solvation of the magnesium centre, with direct implications for the activation and mechanism of the Schlenk exchange. With respect to dichloro-bridged species, the concentrations of monochloro-bridged and separated species increase with the size of R and the degree of solvation. The energy barrier for ligand exchange increases with the size of R, and it is particularly noticeable for R = t-Bu, in agreement with experimental data. As excess solvation of one of the magnesium centres is associated with the most important reaction pathways, it can be deduced that early formation of monochloro-bridged species favours the Cl/R exchange, especially for large R groups. The formation of these species is favoured by higher Mg solvation for small R groups, and by the steric volume of R itself for large R, pointing at a constructive effect of the solvent and R promoting Cl/R exchange. Our study shows that a full characterisation of the speciation of Grignard reagents in solution is possible at reasonable computational costs with machine-learning potentials.

• Cascella, Michele; Bore, Sigbjørn Løland & Eisenstein, Odile (2025). The fellowship of the Grignard: 21st century computational tools for hundred-year-old chemistry. Chemical Science. ISSN 2041-6520. 16, p. 8196–8216. doi: 10.1039/d5sc01078k. Full text in Research Archive Show summary

  This perspective begins with the discovery of the Grignard reaction by a graduate student in the last years of the 19th century, followed by describing why it has remained largely unexplained for more than a century. From the summary of what has been achieved, focusing on the computational aspects, it is now clear that further studies of the chemistry of any chemical species that is highly sensitive to solvents, such as Group I and II elements, require a holistic approach that includes the solute and the solvent together. Ab initio molecular dynamics, which meets these requirements, has produced some results but has hit hard limits due to its relatively high computational costs. In these days, it is becoming clear that data-driven methods, including machine learning potentials and simulations driven by quantitative on-the-fly calculation of relevant observables, have the potential to better and more completely explore the very large chemical space associated with the presence of a large number of species in solution. These methodologies have the chance to give the keys to enter the challenging and still poorly explored world of chemical species whose behaviour and reactivity are strongly influenced by the solvent and the experimental conditions.

• Cezar, Henrique Musseli; Bodenstein, Tilmann; Sveinsson, Henrik Andersen; Ledum, Morten; Reine, Simen & Bore, Sigbjørn Løland (2025). Learning atomic forces from uncertainty-calibrated adversarial attacks. npj Computational Materials. 11. doi: 10.1038/s41524-025-01703-5. Full text in Research Archive Show summary

  Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials (MLIPs). While already providing great practical value, little is known about the actual prediction errors of MLIPs on adversarial structures and whether these errors can be controlled. We propose the Calibrated Adversarial Geometry Optimization (CAGO) algorithm to discover adversarial structures with user-assigned errors. Through uncertainty calibration, the estimated uncertainty of MLIPs is unified with real errors. By performing geometry optimization for calibrated uncertainty, we reach adversarial structures with the user-assigned target MLIP prediction error. Integrating with active learning pipelines, we benchmark CAGO, demonstrating stable MLIPs that systematically converge structural, dynamical, and thermodynamical properties for liquid water and water adsorption in a metal-organic framework within only hundreds of training structures, where previously many thousands were typically required.

• Zeng, Jinzhe; Zhang, Duo; Peng, Anyang; Zhang, Xiangyu; He, Sensen & Wang, Yan [Show all 47 contributors for this article] (2025). DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials. Journal of Chemical Theory and Computation. ISSN 1549-9618. 21(9), p. 4375–4385. doi: 10.1021/acs.jctc.5c00340. Full text in Research Archive Show summary

  In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks, such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of the DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multibackend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLP packages and of differentiable molecular force fields. This architecture allows seamless back-end switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research.

• Sciortino, F.; Zhai, Y.; Bore, Sigbjørn Løland & Paesani, F. (2025). Constraints on the location of the liquid–liquid critical point in water. Nature Physics. ISSN 1745-2473. 21(3), p. 480–485. doi: 10.1038/s41567-024-02761-0. Full text in Research Archive
• Sciortino, Francesco; Zhai, Yaoguang; Bore, Sigbjørn Løland & Paesani, Francesco (2024). Pinpointing the Location of the Elusive Liquid-Liquid Critical Point in Water. ChemRxiv. ISSN 2573-2293. doi: 10.26434/chemrxiv-2024-dqqws. Full text in Research Archive
• Carrer, Manuel; Cezar, Henrique Musseli; Bore, Sigbjørn Løland; Ledum, Morten & Cascella, Michele (2024). Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics. Journal of Chemical Information and Modeling. ISSN 1549-9596. 64, p. 5510–5520. doi: 10.1021/acs.jcim.4c00564. Full text in Research Archive Show summary

  We develop ∂-HylleraasMD (∂-HyMD), a fully end-to-end differentiable molecular dynamics software based on the Hamiltonian hybrid particle-field formalism, and use it to establish a protocol for automated optimization of force field parameters. ∂-HyMD is templated on the recently released HylleraaasMD software, while using the JAX autodiff framework as the main engine for the differentiable dynamics. ∂-HyMD exploits an embarrassingly parallel optimization algorithm by spawning independent simulations, whose trajectories are simultaneously processed by reverse mode automatic differentiation to calculate the gradient of the loss function, which is in turn used for iterative optimization of the force-field parameters. We show that parallel organization facilitates the convergence of the minimization procedure, avoiding the known memory and numerical stability issues of differentiable molecular dynamics approaches. We showcase the effectiveness of our implementation by producing a library of force field parameters for standard phospholipids, with either zwitterionic or anionic heads and with saturated or unsaturated tails. Compared to the all-atom reference, the force field obtained by ∂-HyMD yields better density profiles than the parameters derived from previously utilized gradient-free optimization procedures. Moreover, ∂-HyMD models can predict with good accuracy properties not included in the learning objective, such as lateral pressure profiles, and are transferable to other systems, including triglycerides.

• Giovanetti, Marinella de; Eliasson, Sondre Hilmar Hopen; Bore, Sigbjørn Løland; Eisenstein, Odile & Cascella, Michele (2024). Morphology of lithium halides in tetrahydrofuran from molecular dynamics with machine learning potentials. Chemical Science. ISSN 2041-6520. doi: 10.1039/d4sc04957h. Full text in Research Archive Show summary

  The preferred structures of lithium halides (LiX, with X = Cl, Br, I) in organic solvents have been the subject of a wide scientific debate, and a large variety of forms has been isolated and characterized by X-ray diffraction. The identified molecular scaffolds for LiX are diverse, often built on (LiX)n rings with a prevalence of rhomboidal arrangements and an appropriate number of solvent or Lewis base molecules coordinating the lithium ions. Much less is known about the structures of LiX in solution, limiting the understanding of the synergistic role of LiX in reactions with various organometallic complexes, as prominently represented by the turbo Grignard reaction. Here, we trained a machine learning potential on ab initio data to explore the complex conformational landscape for systems comprising four LiX moieties in tetrahydrofuran (THF). For all the considered halogens a large number of scaffolds were found at thermally accessible free energy values, indicating that LiX in solution are a diverse ensemble constituted of (LiX)n moieties of various sizes, completed by the appropriate number of coordinating THF. LiCl shows a preference for compact, pseudo-cubane Li4Cl4(THF)4 structures, coexisting with open rings. At concentrations close to the solubility limit, LiCl forms hexagonal structures, in analogy with literature observations on pre-nucleating NaCl. LiBr tends to favour less compact, more solvated aggregates. LiI significantly differs from the two other cases, producing highly solvated, monomeric, dimeric, or linear structures. This study provides a comprehensive view of LiX in organic solvent, revealing dynamical polymorphism that is not easily observable experimentally.

• Giovanetti, Marinella de; Eliasson, Sondre Hilmar Hopen; Bore, Sigbjørn Løland; Eisenstein, Odile & Cascella, Michele (2024). Morphology of Lithium Halides in Tetrahydrofuran from Molecular Dynamics with Machine Learning Potentials. ChemRxiv. ISSN 2573-2293. doi: 10.26434/chemrxiv-2024-mxjqn. Full text in Research Archive
• Zeng, Jinzhe; Zhang, Duo; Lu, Denghui; Mo, Pinghui; Li, Zeyu & Chen, Yixiao [Show all 47 contributors for this article] (2023). DeePMD-kit v2: A software package for deep potential models. Journal of Chemical Physics. ISSN 0021-9606. 159(5). doi: 10.1063/5.0155600. Full text in Research Archive Show summary

  DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

• Sen, Samiran; Ledum, Morten; Bore, Sigbjørn Løland & Cascella, Michele (2023). Soft Matter under Pressure: Pushing Particle-Field Molecular Dynamics to the Isobaric Ensemble. Journal of Chemical Information and Modeling. ISSN 1549-9596. 63, p. 2207–2217. doi: 10.1021/acs.jcim.3c00186. Full text in Research Archive Show summary

  Hamiltonian hybrid particle–field molecular dynamics is a computationally efficient method to study large soft matter systems. In this work, we extend this approach to constant-pressure (NPT) simulations. We reformulate the calculation of internal pressure from the density field by taking into account the intrinsic spread of the particles in space, which naturally leads to a direct anisotropy in the pressure tensor. The anisotropic contribution is crucial for reliably describing the physics of systems under pressure, as demonstrated by a series of tests on analytical and monatomic model systems as well as realistic water/lipid biphasic systems. Using Bayesian optimization, we parametrize the field interactions of phospholipids to reproduce the structural properties of their lamellar phases, including area per lipid, and local density profiles. The resulting model excels in providing pressure profiles in qualitative agreement with all-atom modeling, and surface tension and area compressibility in quantitative agreement with experimental values, indicating the correct description of long-wavelength undulations in large membranes. Finally, we demonstrate that the model is capable of reproducing the formation of lipid droplets inside a lipid bilayer.

• Bore, Sigbjørn Løland & Paesani, Francesco (2023). Realistic phase diagram of water from “first principles” data-driven quantum simulations. Nature Communications. 14. Full text in Research Archive
• Ledum, Morten; Carrer, Manuel; Sen, Samiran; Li, Xinmeng; Cascella, Michele & Bore, Sigbjørn Løland (2023). HylleraasMD: Massively parallel hybrid particle-field molecular dynamics in Python. Journal of Open Source Software (JOSS). 8(84). doi: 10.21105/joss.04149. Full text in Research Archive Show summary

  Molecular dynamics (MD) is a computational methodology in which the dynamical behavior of systems of interacting atoms and molecules is investigated by integrating the corresponding classical equations of motion. The analysis of the molecular trajectories yields an incredibly powerful computational microscope with atomic resolution. While prominent examples of molecular dynamics involving all-atom models exist, many systems operate on time- and lengths scales too large, precluding the use of such an approach. The intrinsic complexity of biological soft-matter systems has necessitated the development of coarse-grained (CG) MD models wherein groups of atoms are treated as individual entities. To probe experimentally relevant length- (nm–μm) and time- (ps–ms) scales, further reduction of computational complexity may be warranted through the removal of explicit particle–particle interactions in favor of particle–density field interactions. Such hybrid particle–field (hPF) models recast the interactions between particle pairs into a system of free particles interacting with an external potential dependent on the density, in analogy with self-consistent field theories. HylleraasMD (named after our affiliate centre, the Hylleraas Centre for Quantum Molecular Sciences) (HyMD) is a Python package capable of highly parallel hPF-MD simulations of a wide range of surfactants and other biological systems in a CG representation. At present, it is the only open-source implementation of the hPF formalism freely available to computational researchers.

• Ledum, Morten; Sen, Samiran; Bore, Sigbjørn Løland & Cascella, Michele (2023). On the equivalence of the hybrid particle-field and Gaussian core models. Journal of Chemical Physics. ISSN 0021-9606. 158(19). doi: 10.1063/5.0145142. Full text in Research Archive Show summary

  Hybrid particle–field molecular dynamics is a molecular simulation strategy, wherein particles couple to a density field instead of through ordinary pair potentials. Traditionally considered a mean-field theory, a momentum and energy-conserving hybrid particle–field formalism has recently been introduced, which was demonstrated to approach the Gaussian Core model potential in the grid-converged limit. Here, we expand on and generalize the correspondence between the Hamiltonian hybrid particle–field method and particle–particle pair potentials. Using the spectral procedure suggested by Bore and Cascella, we establish compatibility to any local soft pair potential in the limit of infinitesimal grid spacing. Furthermore, we document how the mean-field regime often observed in hybrid particle–field simulations is due to the systems under consideration, and not an inherent property of the model. Considering the Gaussian filter form, in particular, we demonstrate the ability of the Hamiltonian hybrid particle–field model to recover all structural and dynamical properties of the Gaussian Core model, including solid phases, a first-order phase transition, and anomalous transport properties. We quantify the impact of the grid spacing on the correspondence, as well as the effect of the particle–field filtering length scale on the emergent particle–particle correlations.

• Ledum, Morten; Bore, Sigbjørn Løland & Cascella, Michele (2020). Automated determination of hybrid particle-field parameters by machine learning. Molecular Physics. ISSN 0026-8976. 118(19-20), p. 1–12. doi: 10.1080/00268976.2020.1785571. Full text in Research Archive Show summary

  The hybrid particle-field molecular dynamics method is an efficient alternative to standard particle-based coarse grained approaches. In this work, we propose an automated protocol for optimisation of the effective parameters that define the interaction energy density functional, based on Bayesian optimisation. The machine-learning protocol makes use of an arbitrary fitness function defined upon a set of observables of relevance, which are optimally matched by an iterative process. Employing phospholipid bilayers as test systems, we demonstrate that the parameters obtained through our protocol are able to reproduce reference data better than currently employed sets derived by Flory-Huggins models. The optimisation procedure is robust and yields physically sound values. Moreover, we show that the parameters are satisfactorily transferable among chemically analogous species. Our protocol is general, and does not require heuristic a posteriori rebalancing. Therefore it is particularly suited for optimisation of reliable hybrid particle-field potentials of complex chemical mixtures, and extends the applicability corresponding simulations to all those systems for which calibration of the density functionals may not be done via simple theoretical models.

• Schäfer, Ken; Kolli, Hima Bindu; Christensen, Mikkel Killingmoe; Bore, Sigbjørn Løland; Diezemann, Gregor & Gauss, Jürgen [Show all 9 contributors for this article] (2020). Supramolecular Packing Drives Morphological Transitions of Charged Surfactant Micelles. Angewandte Chemie International Edition. ISSN 1433-7851. 59(42), p. 18591–18598. doi: 10.1002/anie.202004522. Full text in Research Archive Show summary

  The shape and size of self‐assembled structures upon local organization of their molecular building blocks are hard to predict in the presence of long‐range interactions. Combining small‐angle X‐ray/neutron scattering data, theoretical modelling, and computer simulations, sodium dodecyl sulfate (SDS), over a broad range of concentrations and ionic strengths, was investigated. Computer simulations indicate that micellar shape changes are associated with different binding of the counterions. By employing a toy model based on point charges on a surface, and comparing it to experiments and simulations, it is demonstrated that the observed morphological changes are caused by symmetry breaking of the irreducible building blocks, with the formation of transient surfactant dimers mediated by the counterions that promote the stabilization of cylindrical instead of spherical micelles. The present model is of general applicability and can be extended to all systems controlled by the presence of mobile charges.

• Bore, Sigbjørn Løland & Cascella, Michele (2020). Hamiltonian and alias-free hybrid particle-field molecular dynamics. Journal of Chemical Physics. ISSN 0021-9606. 153(9). doi: 10.1063/5.0020733. Full text in Research Archive Show summary

  Hybrid particle–field molecular dynamics combines standard molecular potentials with density-field models into a computationally efficient methodology that is well-adapted for the study of mesoscale soft matter systems. Here, we introduce a new formulation based on filtered densities and a particle–mesh formalism that allows for Hamiltonian dynamics and alias-free force computation. This is achieved by introducing a length scale for the particle–field interactions independent of the numerical grid used to represent the density fields, enabling systematic convergence of the forces upon grid refinement. Our scheme generalizes the original particle–field molecular dynamics implementations presented in the literature, finding them as limit conditions. The accuracy of this new formulation is benchmarked by considering simple monoatomic systems described by the standard hybrid particle–field potentials. We find that by controlling the time step and grid size, conservation of energy and momenta, as well as disappearance of alias, is obtained. Increasing the particle–field interaction length scale permits the use of larger time steps and coarser grids. This promotes the use of multiple time step strategies over the quasi-instantaneous approximation, which is found to not conserve energy and momenta equally well. Finally, our investigations of the structural and dynamic properties of simple monoatomic systems show a consistent behavior between the present formulation and Gaussian core models.

• Carrer, Manuel; Skrbic, Tatjana; Bore, Sigbjørn Løland; Milano, Giuseppe; Cascella, Michele & Giacometti, Achille (2020). Can Polarity-Inverted Surfactants Self-Assemble in Nonpolar Solvents? Journal of Physical Chemistry B. ISSN 1520-6106. 124, p. 6448–6458. doi: 10.1021/acs.jpcb.0c04842. Full text in Research Archive Show summary

  We investigate the self-assembly process of a surfactant with inverted polarity in water and cyclohexane using both all-atom and coarse-grained hybrid particle-field molecular dynamics simulations. Unlike conventional surfactants, the molecule under study, proposed in a recent experiment, is formed by a rigid and compact hydrophobic adamantane moiety, and a long and floppy triethylene glycol tail. In water, we report the formation of stable inverted micelles with the adamantane heads grouping together into a hydrophobic core and the tails forming hydrogen bonds with water. By contrast, microsecond simulations do not provide evidence of stable micelle formation in cyclohexane. Validating the computational results by comparison with experimental diffusion constant and small-angle X-ray scattering intensity, we show that at laboratory thermodynamic conditions the mixture resides in the supercritical region of the phase diagram, where aggregated and free surfactant states coexist in solution. Our simulations also provide indications as to how to escape this region to produce thermodynamically stable micellar aggregates.

• Nicola, Antonio De; Soares, Thereza; Santos, Denys ES; Bore, Sigbjørn Løland; Sevink, G J Agur & Cascella, Michele [Show all 7 contributors for this article] (2020). Aggregation of Lipid A Variants: A Hybrid Particle-Field Model. Biochimica et Biophysica Acta - General Subjects. ISSN 0304-4165. 1865. doi: 10.1016/j.bbagen.2020.129570. Full text in Research Archive Show summary

  Lipid A is one of the three components of bacterial lipopolysaccharides constituting the outer membrane of Gram-negative bacteria, and is recognized to have an important biological role in the inflammatory response of mammalians. Its biological activity is modulated by the number of acyl-chains that are present in the lipid and by the dielectric medium, i.e., the type of counter-ions, through electrostatic interactions. In this paper, we report on a coarse-grained model of chemical variants of Lipid A based on the hybrid particle-field/molecular dynamics approach (hPF-MD). In particular, we investigate the stability of Lipid A bilayers for two different hexa- and tetra-acylated structures. Comparing particle density profiles along bilayer cross-sections, we find good agreement between the hPF-MD model and reference all-atom simulation for both chemical variants of Lipid A. hPF-MD models of constituted bilayers composed by hexa-acylated Lipid A in water are stable within the simulation time. We further validate our model by verifying that the phase behavior of Lipid A/counterion/water mixtures is correctly reproduced. In particular, hPF-MD simulations predict the correct self-assembly of different lamellar and micellar phases from an initially random distribution of Lipid A molecules with counterions in water. Finally, it is possible to observe the spontaneous formation and stability of Lipid A vesicles by fusion of micellar aggregates.

• Bore, Sigbjørn Løland & Flåtten, Tore Halsne (2020). High-resolution large time-step schemes for inviscid fluid flow. Applied Mathematical Modelling. ISSN 0307-904X. 81, p. 263–278. doi: 10.1016/j.apm.2019.12.006. Full text in Research Archive
• Bore, Sigbjørn Løland; Kolli, Hima Bindu; Nicola, Antonio De; Byshkin, Maksym; Kawakatsu, Toshihiro & Milano, Giuseppe [Show all 7 contributors for this article] (2020). Hybrid Particle-Field Molecular Dynamics Under Constant Pressure. Journal of Chemical Physics. ISSN 0021-9606. 152(18). doi: 10.1063/5.0007445. Full text in Research Archive Show summary

  Hybrid particle-field methods are computationally efficient approaches for modeling soft matter systems. So far, applications of these methodologies have been limited to constant volume conditions. Here, we reformulate particle-field interactions to represent systems coupled to constant external pressure. First, we show that the commonly used particle-field energy functional can be modified to model and parameterize the isotropic contributions to the pressure tensor without interfering with the microscopic forces on the particles. Second, we employ a square gradient particle-field interaction term to model non-isotropic contributions to the pressure tensor, such as in surface tension phenomena. This formulation is implemented within the hybrid particle-field molecular dynamics approach and is tested on a series of model systems. Simulations of a homogeneous water box demonstrate that it is possible to parameterize the equation of state to reproduce any target density for a given external pressure. Moreover, the same parameterization is transferable to systems of similar coarse-grained mapping resolution. Finally, we evaluate the feasibility of the proposed approach on coarse-grained models of phospholipids, finding that the term between water and the lipid hydrocarbon tails is alone sufficient to reproduce the experimental area per lipid in constant-pressure simulations and to produce a qualitatively correct lateral pressure profile.

• Franco-Ulloa, Sebastian; Tatulli, Giuseppina; Bore, Sigbjørn Løland; Moglianetti, Mauro; Pompa, Pier Paolo & Cascella, Michele [Show all 7 contributors for this article] (2020). Dispersion state phase diagram of citrate-coated metallic nanoparticles in saline solutions. Nature Communications. 11. doi: 10.1038/s41467-020-19164-3. Full text in Research Archive Show summary

  Abstract The fundamental interactions underlying citrate-mediated chemical stability of metal nanoparticles, and their surface characteristics dictating particle dispersion/aggregation in aqueous solutions, are largely unclear. Here, we developed a theoretical model to estimate the stoichiometry of small, charged ligands (like citrate) chemisorbed onto spherical metallic nanoparticles and coupled it with atomistic molecular dynamics simulations to define the uncovered solvent-accessible surface area of the nanoparticle. Then, we integrated coarse-grained molecular dynamics simulations and two-body free energy calculations to define dispersion state phase diagrams for charged metal nanoparticles in a range of medium’s ionic strength, a known trigger for aggregation. Ultraviolet-visible spectroscopy experiments of citrate-capped nanocolloids validated our predictions and extended our results to nanoparticles up to 35 nm. Altogether, our results disclose a complex interplay between the particle size, its surface charge density, and the ionic strength of the medium, which ultimately clarifies how these variables impact colloidal stability.

• Bore, Sigbjørn Løland; Kolli, Hima Bindu; Kawakatsu, Toshihiro; Milano, Giuseppe & Cascella, Michele (2019). Mesoscale Electrostatics Driving Particle Dynamics in Nonhomogeneous Dielectrics. Journal of Chemical Theory and Computation. ISSN 1549-9618. 15(3), p. 2033–2041. doi: 10.1021/acs.jctc.8b01201. Full text in Research Archive
• Kolli, Hima Bindu; Nicola, Antonio De; Bore, Sigbjørn Løland; Schäfer, Ken; Diezemann, Gregor & Gauss, Jürgen [Show all 11 contributors for this article] (2018). Hybrid particle-field molecular dynamics simulations of charged amphiphiles in an aqueous environment. Journal of Chemical Theory and Computation. ISSN 1549-9618. 14(9), p. 4928–4937. doi: 10.1021/acs.jctc.8b00466. Full text in Research Archive Show summary

  We develop and test specific coarse-grained models for charged amphiphilic systems such as palmitoyloleoylphosphatidylglycerol (POPG) lipid bilayer and sodium dodecyl sulfate (SDS) surfactant in an aqueous environment, to verify the ability of the hybrid particle-field method to provide a realistic description of polyelectrolytes. According to the hybrid approach, the intramolecular interactions are treated by a standard molecular Hamiltonian, and the nonelectrostatic intermolecular forces are described by density fields. Electrostatics is introduced as an additional external field obtained by a modified particle-mesh Ewald procedure, as recently proposed [Zhu et al. Phys. Chem. Chem. Phys. 2016, 18, 9799]. Our results show that, upon proper calibration of key parameters, electrostatic forces can be correctly reproduced. Molecular dynamics simulations indicate that the methodology is robust with respect to the choice of the relative dielectric constant, yielding the same correct qualitative behavior for a broad range of values. In particular, our methodology reproduces well the organization of the POPG bilayer, as well as the SDS concentration-dependent change in the morphology of the micelles from spherical to microtubular aggregates. The inclusion of explicit electrostatics with good accuracy and low computational cost paves the way for a significant extension of the hybrid particle-field method to biological systems, where the polyelectrolyte component plays a fundamental role for both structural and dynamical molecular properties. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, copyright © American Chemical Society after peer review and technical editing by the publisher.

• Bore, Sigbjørn Løland; Milano, Giuseppe & Cascella, Michele (2018). Hybrid Particle-Field Model for Conformational Dynamics of Peptide Chains. Journal of Chemical Theory and Computation. ISSN 1549-9618. 14(2), p. 1120–1130. doi: 10.1021/acs.jctc.7b01160. Full text in Research Archive Show summary

  We propose the first model of a polypeptide chain based on a hybrid-particle field approach. The intramolecular potential is built on a two-bead coarse grain mapping for each amino acid. We employ a combined potential for the bending and the torsional degrees of freedom that ensures the stabilization of secondary structure elements in the conformational space of the polypeptide. The electrostatic dipoles associated with the peptide bonds of the main chain are reconstructed by a topological procedure. The intermolecular interactions comprising both the solute and the explicit solvent are treated by a density functional-based mean-field potential. Molecular dynamics simulations on a series of test systems show how the model here introduced is able to capture all the main features of polypeptides. In particular, homopolymers of different lengths yield a complex folding phase diagram, covering from the collapsed to swollen state. Moreover, simulations on models of a four-helix bundle and of an alpha + beta peptide evidence how the collapse of the hydrophobic core drives the appearance of both folded motifs and the stabilization of tertiary or quaternary assemblies. Finally, the polypeptide model is able to structurally respond to the environmental changes caused by the presence of a lipid bilayer. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, copyright © American Chemical Society after peer review and technical editing by the publisher.

• Bore, Sigbjørn Løland; Schindler, Michael; Lam, Khanh-Dang Nguyen Thu; Bertin, Eric & Dauchot, Olivier (2016). Coupling spin to velocity: collective motion of Hamiltonian polar particles. Journal of Statistical Mechanics: Theory and Experiment. doi: 10.1088/1742-5468/2016/03/033305. Full text in Research Archive Show summary

  We propose a conservative two-dimensional particle model in which particles carry a continuous and classical spin. The model includes standard ferromagnetic interactions between spins of two different particles, and a nonstandard coupling between spin and velocity of the same particle inspired by the coupling observed in self-propelled hard discs. Because of this coupling Galilean invariance is broken and the conserved linear momentum associated to translation invariance is not proportional to the velocity of the center of mass. Also, the dynamics is not invariant under a global rotation of the spins alone. This, in principle, leaves room for collective motion and thus raises the question whether collective motion can arise in Hamiltonian systems. We study the statistical mechanics of such a system, and show that, in the fully connected (or mean-field) case, a transition to collective motion does exist in spite of momentum conservation. Interestingly, the velocity of the center of mass, which in the absence of Galilean invariance, is a relevant variable, also feeds back on the magnetization properties, as it acts as an external magnetic field that smoothens the transition. Molecular dynamics simulations of finite size systems indeed reveal a rich phase diagram, with a transition from a disordered to a homogeneous polar phase, but also more complex inhomogeneous phases with local order interrupted by topological defects.

View all works in NVA

• Sveinsson, Henrik Andersen; Guren, Marthe Grønlie & Bore, Sigbjørn Løland (2025). CO2Basalt. Full text in Research Archive
• Bore, Sigbjørn Løland (2024). Realistic phase diagram of water from first principles. Full text in Research Archive
• Eliasson, Sondre Hilmar Hopen; Giovanetti, Marinella de; Bore, Sigbjørn Løland; Cascella, Michele & Eisenstein, Odile (2024). Machine Learned Potential for Organolithium Compounds in THF. Full text in Research Archive
• Eliasson, Sondre Hilmar Hopen; Giovanetti, Marinella de; Bore, Sigbjørn Løland; Cascella, Michele & Eisenstein, Odile (2024). Machine Learned Potential for Organolithium Compounds in THF. Full text in Research Archive
• Eliasson, Sondre Hilmar Hopen; Giovanetti, Marinella de; Bore, Sigbjørn Løland; Bortoli, Marco; Cascella, Michele & Eisenstein, Odile (2023). Machine Learned Potential for Organolithium in THF. Full text in Research Archive
• Eliasson, Sondre Hilmar Hopen; Giovanetti, Marinella de; Bore, Sigbjørn Løland; Bortoli, Marco & Cascella, Michele (2023). Machine Learned Potential for Organolithium Compounds in THF. Full text in Research Archive
• Guren, Marthe Grønlie; Bore, Sigbjørn Løland; Renard, Francois & Sveinsson, Henrik Andersen (2026). Energy dissipation at the atomic scale explains how fracture energy depends on crack velocity in silica glass. arxiv.org. doi: 10.48550/ARXIV.2605.03457. Full text in Research Archive Show summary

  The fracture energy of brittle materials rises with crack velocity, and this effect is typically attributed to surface roughening from path instabilities. Here we show, using molecular dynamics simulations of silica glass with a first-principles machine learned interatomic potential, that the structural fracture energy rises by up to 33 % already below the branching threshold, showing that fracture energy is not a constant material property. This rise in fracture energy is roughly equally partitioned between an increase in the intrinsic surface energy density and nanoscale roughening that increases the real fracture surface area. Results demonstrate that dynamic fracture in silica glass increases the fracture energy not merely by creating more apparent surface, but also by creating a fundamentally different surface at the nanoscale.

• Bore, Sigbjørn Løland; Persson, B. N. J. & Sveinsson, Henrik Andersen (2026). Why ice is so slippery. arxiv.org. doi: 10.48550/ARXIV.2603.11539. Full text in Research Archive Show summary

  We simulate nanoscale ice-glass friction from first principles and upscale it by modeling frictional heating. Nanoscale simulations alone overestimate friction and miss its velocity dependence. With frictional heating, the contact warms toward the melting point, thickening the film tenfold and cutting its viscosity a hundredfold; friction drops with velocity, matching experiment. Premelting forms the film without sliding or heating, but its slipperiness hinges on frictional heating, as Bowden and Hughes proposed in 1939, without melting. Snow, other materials and coated surfaces remain open.

• Bore, Sigbjørn Løland; Cascella, Michele & Kvaal, Simen (2020). Advances in the Hybrid Particle-Field Approach: Towards Biological Systems. Universitetet i Oslo. Full text in Research Archive Show summary

  This dissertation aims at advancing the capability of hybrid particle-field simulations of representing various physical phenomena relevant to biological systems. While hybrid particle-field simulations are computationally efficient and well adapted for studying mesoscale systems with molecular resolution, this approach has so far predominantly been applied to simple polymers. The computational investigation of systems of higher complexity, such as DNA and proteins, requires development of new models and an extension of the hybrid particle-field methodology. To this end, six research papers are presented. The main research output of these papers consists in both new methods for representing electrostatics and constant-pressure conditions, and new models for proteins and charged lipids within the hybrid particle-field formalism. The work contained in this thesis thus provides key steps towards large-scale realistic representations of biological systems

• Bore, Sigbjørn Løland; Støvneng, Jon Andreas & Flåtten, Tore (2015). High-Resolution Large Time-Step Schemes for Hyperbolic Conservation Laws. Norges teknisk-naturvitenskapelige universitet. Full text in Research Archive Show summary

  This thesis is concerned with numerical methods for solving hyperbolic conservation laws. A generalization of large time-step schemes (LTSS) to high resolution is presented. The generalization is based on the previous work in [Lindquist, 2014; Harten, 1986]. Starting from a general LTSS, a set of sufficient conditions for conservative, consistent, and total variation diminishing (TVD) LTSS are derived. Second-order accuracy away from discontinuities is achieved by a modified flux approach. Such an approach is shown to be TVD whenever a supplementary condition is satisfied. The full set of criteria constitutes a new framework of sufficient conditions for high-resolution LTSS. By application of this framework on the large time-step Roe scheme (LTS-Roe1), a new second-order version (LTS-Roe2) is proposed. Further, to overcome the problems of LTS-Roe1 and LTS-Roe2 with transonic rarefaction, a hybrid scheme of LTS-Roe and Lax-Friedrichs is proposed (Hybrid). The methods are investigated and compared against the second-order LTS-Harten. This is done by numerical studies on Burgers equation and on the Euler equations. Numerical tests for continuous initial conditions show second-order convergence for all methods. For discontinuous initial conditions LTS-Roe2 has better accuracy than LTS-Roe1- however, this difference becomes small for high CFL-numbers. LTS-Roe2 is shown to have a very good resolution of discontinuities, but for high CF L-numbers it produces spurious oscillations for the Euler equations. Hybrid is more diffusive, but has no problems with transonic rarefaction. Tests show that LTS-Harten consistently gives good results with less oscillations than LTS-Roe2, but it has, however, a tendency to smear out discontinuities when the CFL-number is increased.

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